Score an organisation's or team's readiness to adopt AI tooling across six gating dimensions — data, tooling, skills, governance, process, and culture — with an evidence line per score and the blocking gaps ranked by what stops adoption first. Use when the user asks "are we ready for AI", wants an AI maturity or readiness baseline, or needs to know what to fix before rolling tools out. Do NOT use to pick which use cases to build (that's ai-use-case-triage) or to plan the rollout itself (that's ai-adoption-rollout).
In Claude Code, with the ai-engineering plugin installed, invoke it as:
/ai-engineering:ai-readiness-assessment
Try it
A realistic prompt from the skill's acceptance evals:
We're a 200-person fintech. Engineering has Copilot licences that about a third of people use, we have no written AI policy, our docs are spread across Confluence and Notion, and leadership keeps asking when we'll 'be an AI company'. Are we ready?
What a good result looks like
Every score carries an evidence line. A number with nothing behind it is the failure mode this skill exists to prevent — cut it or mark it [insufficient evidence].
All six dimensions appear, including the ones the input is thin on. Silence on a dimension reads as a pass, and that's how readiness reports mislead.
Gaps are ranked by blocking order, not by score. The lowest number is not automatically the first problem, and the plan must say why.
Every gap has a first action and an owner role — a role, never a person's name, so the assessment doesn't go stale when someone changes teams.
The posture sentence takes a position and names the condition that would change it. "It depends" is a refusal to do the job.
No invented facts. Where the input is silent, say so; a fabricated observation is worse than an admitted gap.
How it's checked
Measured against plain Claude Code (2026-10-23): it met 75% of its checks, against 38% without it, and a blind judge preferred the agent without it. How skills are checked
20 assertions across 3 acceptance evals, and 8 trigger queries. Every skill's checks
What the output must do
All six dimensions (data, tooling, skills, governance, process, culture) are scored 0-4 — none is omitted
Every score carries a one-line evidence reference drawn from the input, or is marked [insufficient evidence: <what to collect>]
Gaps are ranked by what blocks adoption first, with the dependency named — not simply sorted by lowest score
Every gap has a concrete first action and an owner expressed as a role, not a person's name
The posture sentence takes a position and names the condition that would change it
No facts are invented beyond what the prompt supplies
It does not accept that the data is ready: 38% of mains records are still in spreadsheets, and they are the cast-iron mains where most bursts happen, so burst prediction is held back by that data (and the untested FieldLog history)
It corrects the training figure: 120 of 300 staff is 40%, not 52%
It says there is no approved AI policy (only draft v0.3, due at committee in January 2027), despite what the CEO and the Customer Service Lead said
It notes that the billing system has no API, with 9 months quoted to add one, as an obstacle for the billing assistant
It does not claim Kestrel is ahead of or behind other water companies, and says the inputs contain no comparison data
It does not invent facts or figures that are not in the files
It fits on one page (roughly 500 words or fewer)
It gives a direct answer on readiness in this reply (for example, closer to ready for some uses than others), not only a list of questions
It treats the front desk and clinical notes differently, with clinical notes needing more care, such as a clinician checking every note
It flags the 2014 records system as something to check (whether it can connect to new tools or export data) without asserting that it can or cannot
It raises the handling of patients' information and who in the practice decides and is responsible, without stating specific laws as fact
It gives at least one concrete first step
It does not invent product names, prices or features
It is a few bullets
When it should and shouldn't activate
Should activate:
Are we ready to adopt AI?
Assess our AI maturity and tell me what's blocking us
Give me an AI readiness baseline for our engineering org
What do we need to fix before rolling out AI tools?